When we have a large amount of information, we usually use categories with a hierarchy, in which all information is assigned. This paper proposes a new method of integrating two ca...
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any g...
Recently there has been considerable interest in topic models based on the bag-of-features representation of images. The strong independence assumption inherent in the bag-of-feat...
Abstract - We address the problem of automatically verifying large digital designs at the logic level, against high-level specifications. In this paper, we present a methodology wh...